Publishing statistical models: Getting the most out of particle physics experiments

Cranmer, Kyle; Kraml, Sabine; Prosper, Harrison B.; Bechtle, Philip; Bernlochner, Florian U.; Bloch, Itay M.; Canonero, Enzo; Chrzaszcz, Marcin; Coccaro, Andrea; Conrad, Jan; Cowan, Glen; Feickert, Matthew; Ferreiro, Nahuel F.; Fowlie, Andrew; Heinrich, Lukas; Held, Alexander; Kuhr, Thomas; Kvellestad, Anders; Madigan, Maeve; Mahmoudi, Farvah; Mora, Knut D.; Neubauer, Mark S.; Pierini, Maurizio; Rojo, Juan; Sekmen, Sezen; Silvestrini, Luca; Sanz, Veronica; Stark, Giordon; Torre, Riccardo; Thorne, Robert; Waltenberger, Wolfgang; Wardle, Nicholas und Wittbrodt, Jonas (2022): Publishing statistical models: Getting the most out of particle physics experiments. In: Scipost Physics, Bd. 12, Nr. 1, 37

Abstract

The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases - including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits - we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results. (C) Copyright K. Cranmer et al. This work is licensed under the Creative Commons Attribution 4.0 International License. Published by the SciPost Foundation.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Physik
• Themengebiete: 500 Naturwissenschaften und Mathematik > 530 Physik
• ISSN: 2542-4653
• Sprache: Englisch
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 390783311